Abstract
Computationally creative systems for music have recently achieved
impressive results, fuelled by progress in generative machine learning. However, black-box approaches have raised fundamental concerns for ethics, accountability, explainability, and musical plausibility. To enable trustworthy machine creativity, we introduce the Harmonic Memory, a Knowledge Graph (KG) of harmonic patterns extracted from a large and heterogeneous musical corpus. By leveraging a cognitive model of tonal harmony, chord progressions are segmented into meaningful structures, and patterns emerge from their comparison via harmonic similarity. Akin to a music memory, the KG holds temporal connections between consecutive patterns, as well as salient similarity relationships. After demonstrating the validity of our choices, we provide examples of how this design enables novel pathways for combinational creativity. The memory provides a fully accountable and explainable framework to inspire and support creative professionals – allowing for the discovery of progressions consistent with given criteria, the recomposition of harmonic sections, but also the co-creation of new progressions
impressive results, fuelled by progress in generative machine learning. However, black-box approaches have raised fundamental concerns for ethics, accountability, explainability, and musical plausibility. To enable trustworthy machine creativity, we introduce the Harmonic Memory, a Knowledge Graph (KG) of harmonic patterns extracted from a large and heterogeneous musical corpus. By leveraging a cognitive model of tonal harmony, chord progressions are segmented into meaningful structures, and patterns emerge from their comparison via harmonic similarity. Akin to a music memory, the KG holds temporal connections between consecutive patterns, as well as salient similarity relationships. After demonstrating the validity of our choices, we provide examples of how this design enables novel pathways for combinational creativity. The memory provides a fully accountable and explainable framework to inspire and support creative professionals – allowing for the discovery of progressions consistent with given criteria, the recomposition of harmonic sections, but also the co-creation of new progressions
| Original language | English |
|---|---|
| Title of host publication | WWW '23: Proceedings of the ACM Web Conference 2023 |
| Publication status | Published - 2023 |
| Event | ACM Web Conference 2023 - Austin, TX, USA Duration: 1 May 2022 → … |
Conference
| Conference | ACM Web Conference 2023 |
|---|---|
| Period | 1/05/2022 → … |
Keywords
- sound and music computing
- ontology engineering